Using Facebook Topic Data to Refine an Advertising Campaign
You might have seen our recent blog post where we discussed how an agency used Facebook topic data to carry out audience research and refine an upcoming ad campaign. In this post we'll take a look at how the research was carried using our platform.
Using Facebook topic data to understand an audience
It naturally follows that the better you can understand your audience, the better you can plan your ad campaign, and therefore the more impact your campaign will have.
In this instance...
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Nested Analysis Queries in PYLON
In this blog I'm going to introduce nested analysis queries in PYLON. These are queries that allow you to delve more than one level deep. The example I'll use will analyze authors by gender and then divide them further into age demographic ranges.
PYLON is DataSift's privacy first technology that allows you to look at Facebook data on an aggregate level without visibility of the details of individual people. Think of it like this: you can hear what a crowd is saying without zooming...
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Investigating Audience Snacking Habits with Facebook Topic Data
You might have seen our recent blog post where we highlighted some surprising findings about snacking habits based on research using Facebook topic data. In this post we'll take a look at how the research was carried using our platform.
Testing long-held assumptions
We're all increasingly looking to make better data-informed decisions. In this case an agency working on behalf of a brand of popular snack was looking to improve the effectiveness of their advertising around the time of...
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Important Announcement on API Versioning
As the DataSift platform continues to develop so does inevitably our API. As we have a full roadmap of new products and features coming up this feels like a good time to clarify how we version our API and how you can best keep up with the changes.
You'll see below that we're planning to deprecate a couple of API versions over the coming months, so please take a moment to read about the changes.
We've created some new resources to help you stay up-to-date....
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Validating Interaction Filters with Facebook Super Public Text Samples
DataSift PYLON for Facebook Topic Data allows you to analyze audiences on Facebook whilst protecting users' privacy. To help you build more accurate analysis we're introducing 'Super Public' text samples for Facebook. You can use Super Public text samples to validate your interaction filters to check you are recording the correct data into your index for analysis. You can also use these text samples to train machine learned classifiers. In this post we'll take a look...
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